Create Known Spatial Pattern for Selected Genes

runPatternSimulation()

Creates a known spatial pattern for selected genes one-by-one and runs the different spatial gene detection tests.

runPatternSimulation(
    gobject,
    pattern_name = "pattern",
    pattern_colors = c(`in` = "green", out = "red"),
    pattern_cell_ids = NULL,
    gene_names = NULL,
    spatial_probs = c(0.5, 1),
    reps = 2,
    spatial_network_name = "kNN_network",
    spat_methods = c("binSpect_single", "binSpect_multi", "spatialDE", "spark",
        "silhouetteRank"),
    spat_methods_params = list(NA, NA, NA, NA, NA),
    spat_methods_names = c("binSpect_single", "binSpect_multi", "spatialDE", "spark",
        "silhouetteRank"),
    scalefactor = 6000,
    save_plot = T,
    save_raw = T,
    save_norm = T,
    save_dir = "~",
    max_col = 4,
    height = 7,
    width = 7,
    run_simulations = TRUE,
    ...
)

Arguments

gobject	giotto object
pattern_name	name of spatial pattern
pattern_colors	2 color vector for the spatial pattern
pattern_cell_ids	cell ids that make up the spatial pattern
gene_names	selected genes
spatial_probs	probabilities to test for a high expressing gene value to be part of the spatial pattern
reps	number of random simulation repetitions
spatial_network_name	which spatial network to use for binSpectSingle
spat_methods	vector of spatial methods to test
spat_methods_params	list of parameters list for each element in the vector of spatial methods to test
spat_methods_names	name for each element in the vector of spatial elements to test
scalefactor	library size scaling factor when re-normalizing dataset
save_plot	save intermediate random simulation plots or not
save_raw	save the raw expression matrix of the simulation
save_norm	save the normalized expression matrix of the simulation
save_dir	directory to save results to
max_col	maximum number of columns for final plots
height	height of final plots
width	width of final plots
run_simulations	run simulations (default = TRUE)
…	additional parameters for renormalization

Value

A data.table with results.